A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-33904-3_47 http://hdl.handle.net/11449/198202 |
Resumo: | In this paper we present a method based on genetic algorithm capable of analyzing a significant number of features obtained from fractal techniques, Haralick texture features and curvelet coefficients, as well as several selection methods and classifiers for the study and pattern recognition of colorectal cancer. The chromosomal structure was represented by four genes in order to define an individual. The steps for evaluation and selection of individuals as well as crossover and mutation were directed to provide distinctions of colorectal cancer groups with the highest accuracy rate and the smallest number of features. The tests were performed with features from histological images H&E, different values of population and iterations numbers and with the k-fold cross-validation method. The best result was provided by a population of 500 individuals and 50 iterations applying relief, random forest and 29 features (obtained mainly from the combination of percolation measures and curvelet subimages). This solution was capable of distinguishing the groups with an accuracy rate of 90.82% and an AUC equal to 0.967. |
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A Model Based on Genetic Algorithm for Colorectal Cancer DiagnosisColorectal cancerFeature classificationFeature selectionGenetic algorithmIn this paper we present a method based on genetic algorithm capable of analyzing a significant number of features obtained from fractal techniques, Haralick texture features and curvelet coefficients, as well as several selection methods and classifiers for the study and pattern recognition of colorectal cancer. The chromosomal structure was represented by four genes in order to define an individual. The steps for evaluation and selection of individuals as well as crossover and mutation were directed to provide distinctions of colorectal cancer groups with the highest accuracy rate and the smallest number of features. The tests were performed with features from histological images H&E, different values of population and iterations numbers and with the k-fold cross-validation method. The best result was provided by a population of 500 individuals and 50 iterations applying relief, random forest and 29 features (obtained mainly from the combination of percolation measures and curvelet subimages). This solution was capable of distinguishing the groups with an accuracy rate of 90.82% and an AUC equal to 0.967.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Department of Computer Science and Statistics São Paulo State University (UNESP), R. Cristovão Colombo, 2265Faculty of Computation (FACOM) Federal University of Uberlândia (UFU), Av. João Naves de Ávila, 2121Center of Mathematics Computing and Cognition Federal University of ABC (UFABC), Av. dos Estados, 5001Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira S/NDepartment of Computer Science and Statistics São Paulo State University (UNESP), R. Cristovão Colombo, 2265CNPq: #304848/2018-2CNPq: #313365/2018-0CNPq: #427114/2016-0CNPq: #430965/2018-4FAPEMIG: #APQ-00578-18Universidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Universidade Federal do ABC (UFABC)Federal Institute of Triângulo Mineiro (IFTM)Taino, Daniela F. [UNESP]Ribeiro, Matheus G. [UNESP]Roberto, Guilherme FreireZafalon, Geraldo F. D. [UNESP]do Nascimento, Marcelo ZanchettaTosta, Thaína A.Martins, Alessandro S.Neves, Leandro A. [UNESP]2020-12-12T01:06:21Z2020-12-12T01:06:21Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject504-513http://dx.doi.org/10.1007/978-3-030-33904-3_47Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 504-513.1611-33490302-9743http://hdl.handle.net/11449/19820210.1007/978-3-030-33904-3_472-s2.0-85075660821Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2021-10-23T09:55:31Zoai:repositorio.unesp.br:11449/198202Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T09:55:31Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
title |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
spellingShingle |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis Taino, Daniela F. [UNESP] Colorectal cancer Feature classification Feature selection Genetic algorithm |
title_short |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
title_full |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
title_fullStr |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
title_full_unstemmed |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
title_sort |
A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis |
author |
Taino, Daniela F. [UNESP] |
author_facet |
Taino, Daniela F. [UNESP] Ribeiro, Matheus G. [UNESP] Roberto, Guilherme Freire Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Zanchetta Tosta, Thaína A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
author_role |
author |
author2 |
Ribeiro, Matheus G. [UNESP] Roberto, Guilherme Freire Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Zanchetta Tosta, Thaína A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Uberlândia (UFU) Universidade Federal do ABC (UFABC) Federal Institute of Triângulo Mineiro (IFTM) |
dc.contributor.author.fl_str_mv |
Taino, Daniela F. [UNESP] Ribeiro, Matheus G. [UNESP] Roberto, Guilherme Freire Zafalon, Geraldo F. D. [UNESP] do Nascimento, Marcelo Zanchetta Tosta, Thaína A. Martins, Alessandro S. Neves, Leandro A. [UNESP] |
dc.subject.por.fl_str_mv |
Colorectal cancer Feature classification Feature selection Genetic algorithm |
topic |
Colorectal cancer Feature classification Feature selection Genetic algorithm |
description |
In this paper we present a method based on genetic algorithm capable of analyzing a significant number of features obtained from fractal techniques, Haralick texture features and curvelet coefficients, as well as several selection methods and classifiers for the study and pattern recognition of colorectal cancer. The chromosomal structure was represented by four genes in order to define an individual. The steps for evaluation and selection of individuals as well as crossover and mutation were directed to provide distinctions of colorectal cancer groups with the highest accuracy rate and the smallest number of features. The tests were performed with features from histological images H&E, different values of population and iterations numbers and with the k-fold cross-validation method. The best result was provided by a population of 500 individuals and 50 iterations applying relief, random forest and 29 features (obtained mainly from the combination of percolation measures and curvelet subimages). This solution was capable of distinguishing the groups with an accuracy rate of 90.82% and an AUC equal to 0.967. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T01:06:21Z 2020-12-12T01:06:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-33904-3_47 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 504-513. 1611-3349 0302-9743 http://hdl.handle.net/11449/198202 10.1007/978-3-030-33904-3_47 2-s2.0-85075660821 |
url |
http://dx.doi.org/10.1007/978-3-030-33904-3_47 http://hdl.handle.net/11449/198202 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 504-513. 1611-3349 0302-9743 10.1007/978-3-030-33904-3_47 2-s2.0-85075660821 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
504-513 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965010248597504 |